It is going to be near-impossible for me to be objective about Dak Prescott. He is a Dallas Cowboy and he graduated from the same high school I did. He’s probably the biggest sports star that part of Louisiana has had since Joe Delaney.

In recent years, Chris Brown of Smart Football has been talking plenty about package plays and after Dak’s performance in the first preseason game of the year, he analyzed one play from the game. It’s good enough I recommend it. Please read, it’s worth your time.


I didn’t expect another trade of this magnitude, and so quickly. But let’s crunch the numbers on this trade, and compare them to the 2016 Titans-Rams trade.

The Browns received from the Eagles, the #8, #77 and #100 picks in this draft. In 2017 they receive the Eagles first round pick. In 2018 they receive the Eagles 2nd round pick. The Eagles have received the #2 pick in this draft, and the Browns 4th round pick in 2017.

For the purposes of this calculation, we assume the Eagles will pick 20th in 2017 and 2018, and that the Brown in 2017 will rise from 2nd to 10th.


The AV costs of the 2016 Eagles Browns trade.
Eagles Browns Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
2 46 8 40
(138) 8 77 12
100 17
(20) 29
(52) 22
Total 54 Total 120
66 2.22


The Delta AV for both trades are the same, but since the Eagles received a lot less AV, the relative ratio of AV given to AV received is higher. The trade cost is the same, but the purchase is more highly leveraged.

Determining how to assess draft trades in the NFL is not hard (see here, here, and here). Ever since Pro Football Reference went through the trouble of determining what average AV can be assigned to a draft slot, it’s merely a matter of counting. The technique has some variance, as the draft slot of a future pick is not known. Even so, with a bit of conservative extrapolation, you can still get a feel for the overall cost of a trade.


First, the numbers:


The AV costs of the 2016 Rams Titans trade.
Rams Titans Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
1 51 15 28
113 14 43 24
177 5 45 25
76 17
(20) 29
(84) 13
Total 70 Total 136
66 1.94


In the data above, we assume that the Rams will improve 5 slots in draft placement, so that the first and third they sent to the Titans would be picks 20 and 84. If the Titans end up 18th or 23rd, it’s notable that the difference in value at this point is less than the point-to-point deviation, so that kind of change won’t affect the calculation much. Pro Football Reference’s raw data are moderately noisy.

The Rams total investment is 136 AV, roughly equal to the career value of John Elway. That’s not entirely accurate, as the Rams actually received three picks in return, and if the other two return 19, then the player they pick at #1, to return the value of the investment, only has to yield 117 AV.Now, 117 points is about mid in between Phillip Rivers and Aaron Rogers in value.

Update: Johnny Unitas, at 114, is a closer comparable.

In terms of risk, the trade is riskier than the Eli Manning trade, and less risky than the RG III trade or the Earl Campbell trade. For 9 more AV than the RG III trade, they received 24 more AV in return.

Best of luck to the Rams. I hope their picks work out well for them.

Odds for the 2015 NFL playoff final, presented from the AFC team’s point of view:

SuperBowl Playoff Odds
Prediction Method AFC Team NFC Team Score Diff Win Prob Est. Point Spread
C&F Playoff Model Denver Broncos Carolina Panthers 2.097 0.891 15.5
Pythagorean Expectations Denver Broncos Carolina Panthers -0.173 0.295 -6.4
Simple Ranking Denver Broncos Carolina Panthers -2.3 0.423 -2.3
Median Point Spread Denver Broncos Carolina Panthers -5.0 0.337 -5.0


Last week the system went 1-1, for a total record of 6-4. The system favors Denver more than any other team, and does not like Carolina at all. Understand, when a team makes it to the Super Bowl easily, and a predictive system gave them about a 3% chance to get there in the first place, it’s reasonable to assume that in that instance, the system really isn’t working.

So we’re going to modify our table a little bit and give some other predictions and predictive methods. The first is the good old Pythagorean formula. We best fit the Pythagorean exponent to the data for the year, so there is good reason to believe that it is more accurate than the old 2.37. It favors Carolina by a little more than six points. SRS directly gives point spread, which can be back calculated into a 57.7% chance of Carolina winning. Likewise, using median point spreads to predict the Denver-Carolina game gives Carolina a 66.3% chance of winning.

Note that none of these systems predicted the outcome of the Carolina – Arizona game. Arizona played a tougher schedule and was more of a regular season statistical powerhouse than Carolina. Arizona, however, began to lose poise as it worked its way through the playoffs. And it lost a lot of poise in the NFC championship game.

Odds for the third week of the 2015 playoffs, presented from the home team’s point of view:

Conference Championship Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Carolina Panthers Arizona Cardinals -1.40 0.198 -10.4
Denver Broncos New England Patriots 1.972 0.879 14.6


Last week the system went 2-2, for a total record of 5-3. The system favors Arizona markedly, and Denver by an even larger margin. That said, the teams my system does not like have already won one game. There have been years when a team my system didn’t like much won anyway. That was the case in 2009, when my system favored the Colts over the Saints. The system isn’t perfect, and the system is static. It does not take into account critical injuries, morale, better coaching, etc.

Odds for the second week of the 2015 playoffs, presented from the home team’s point of view:

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Carolina Panthers Seattle Seahawks -1.713 0.153 -12.7
Arizona Cardinals Green Bay Packers -0.001 0.500 0.0
Denver Broncos Pittsburgh Steelers 0.437 0.608 3.2
New England Patriots Kansas City Chiefs -0.563 0.363 -4.2


Last week the system went 3-1 and perhaps would have gone 4-0 if after the Burflict interception, Cincinnati had just killed three plays and kicked a field goal.

The system currently gives Seattle a massive advantage in the playoffs. It says that Green Bay/Arizona is effectively an even match up, and that both the AFC games are pretty close. It favors Denver in their matchup, and the Chiefs in theirs.

One last comment about last week’s games. The Cincinnati-Pitt game was the most depressing playoff game I’ve seen in a long time, both for the dirty play on both sides of the ball, and the end being decided by stupid play on Cincinnati’s part.  It took away from the good parts of the game, the tough defense when people weren’t pushing the edges of the rules, and the gritty play on the part of McCarron and Roethlisberger. There was some heroic play on both their parts, in pouring rain.

But for me, watching Ryan Shazier leading with the crown of his helmet and then listening to officials explain away what is obvious on video more or less took the cake. If in any way shape or form, this kind of hit is legal, then the NFL rules system is busted.

The cumulative stats for the 2015 regular season are:


This gives us the basis to generate playoff values based on my playoff formula. Playoff Odds are calculated according to this model:

logit P = 0.668 + 0.348*(delta SOS) + 0.434*(delta Playoff Experience)

and the results are:

2015 NFL Playoff Teams, C&F Worksheet.
Rank Name Home Field Adv Playoff Experience SOS Total Score
1 Carolina Panthers 0.406 0.434 -1.35 -0.51
2 Arizona Cardinals 0.406 0.434 0.456 1.296
3 Minnesota Vikings 0.406 0.0 0.654 1.06
4 Washington Redskins 0.406 0.0 -0.866 -0.46
5 Green Bay Packers 0.0 0.434 0.863 1.297
6 Seattle Seahawks 0.0 0.434 0.769 1.203
1 Denver Broncos 0.406 0.434 0.727 1.567
2 NE Patriots 0.406 0.434 -0.839 0.001
3 Cinncinnati Bengals 0.406 0.434 0.661 1.501
4 Houston Texans 0.406 0.0 -0.828 -0.422
5 Kansas City Chiefs 0.0 0.0 0.564 0.564
6 Pittsburgh Steelers 0.0 0.434 0.696 1.130


The total score of a particular team is used as a base. Subtract the score of the opponent and the result is the logit of the win probability for that game. You can use the inverse logit (see Wolfram Alpha to do this easily) to get the probability, and you can multiply the logit of the win probability by 7.4 to get the estimated point spread.


For the first week of the 2014 playoffs, I’ve done all this for you, in the table below. Odds are presented from the home team’s point of view:

First Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Minnesota Vikings Seattle Seahawks -0.143 0.464 -1.05
Washington Redskins Green Bay Packers -1.757 0.147 -13.0
Cinncinnati Bengals Pittsburgh Steelers 0.371 0.591 2.75
Houston Texans Kansas City Chiefs -0.986 0.271 -7.30


So the system suggests that Minnesota – Seattle should be close, perhaps unbettable. Cinncinnati-Pittsburgh is an even match, with Cinncinnati’s factors amounting to a typical home field advantage. Houston-Kansas City and Washington-GB are predicted to be easy wins for the visiting team.


Get every new post delivered to your Inbox.

Join 252 other followers